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Benefits of Texture Analysis of Dual Energy CT for
Computer–Aided Pulmonary Embolism Detection
A. Foncubierta Rodríguez,
O. Jiménez del Toro, A. Platon, P.A. Poletti, H. Müller,
A. Depeursinge
Pulmonary Embolism
• Obstruction of arteries in the lungs
• Unspecific symptoms
• High mortality rates:
– 75% (initial hospital admission)
– 30% (3 years after discharge)
• Delays in diagnosis increase the risk
• But easily treated with anticoagulants
2
PE Imaging
Material Attenuation Coefficient vs keV
0.
1
1
1
0
1
0
0
40 50 60 70 80 90 100 110 120 130 140
Photon Energy (keV)
m(E)
(cm2/mg)
Iodine
Water
80 keV
140 keV
Conventional CT images
• Wedge shaped regions
• Heterogeneous attenuation
• Correlation with
vascularization and
ventilation
Dual Energy CT images
• 4D Data
• X,Y,Z
• Energy level
• Different materials: different
attenuations
3
Dataset
• 25 patients
• Image resolution
• 0.83mm/voxel
(axial plane)
• 1mm inter-slice distance
• 1.25mm slice thickness
• 11 energy levels
• Manually segmented
lobes
• Qanadli index
4
Pipeline
• Automatic regions of interest
• Region-level features: energy of wavelets
• Lobe-level descriptors: Bag of visual words
• One vocabulary per energy level
3D
Analysis
• Histogram of visual words for all energy-
level vocabularies
• Find optimal combination of energy-level
vocabularies
4D data
integration:
5
Automatic ROIs
• Saliency-based:
– 3D Difference of
Gaussians
– Multiple scales
– Geodesic regional
extrema
• Data-driven region
shape
• Local to global analysis
of the lobes
6
Region-level Features
4 dimensional feature vector per region
Energy
in
Regions
4 scales
3D DoG
7
Bag of visual words
• BOVW allows data-driven features:
– Patterns actually occurring in the data
• Vocabularies
– K-means clustering
– 5 to 25 words
– One vocabulary per energy level
– Lobe specific: lobes are not directly comparable
• Each lobe described by 11 histograms of VW
8
Evaluation
• Classification based on 1-NN
– Q_i > 0
– Q_i < 0
• Leave One Patient Out
• Combinations:
– From 1 to 11 energy levels
– 5 to 50 visual words per energy level
• Reference: 70 KeV for conventional CT
9
Results
Lobe
4D Analysis
Accuracy
Energy levels
Visual
words
Conventional
Accuracy
Lower Right 84% 50+130 KeV 5 52%
Lower Left 84% 100+140 KeV 5 48%
Middle Right 80% 40+50+130+140 Kev 5 52%
Upper Left 76% 40+70+80+90 Kev 25 60%
Upper Right 80% 90+120 KeV 25 56%
10
Conclusions
• Using 4D analysis of DECT outperforms
conventional CT: 36% accuracy increase
• Consistent results among all lobes
• Lobe specificities:
– No optimal parameters for all lobes
– Methods need to be optimized per lobe
• Satisfactory results for integration of
automatic ROI detection
11
Future work
Larger
database
• Ongoing process
Similarity-
based retrieval
• Qanadli index as
metric
Optimize
BOVW
• Synonyms
12
Thanks for your attention!
Questions?
A. Foncubierta-Rodríguez, O. Jiménez del Toro, A. Platon, P.A. Poletti, H.Müller and
A. Depeursinge, Benefits of texture analysis of dual energy CT for computer-
aided pulmonary embolism detection, in: The 35th Annual International Conference
of the IEEE Engineering in Medicine and Biology Society (EMBC 2013), Osaka 2013

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Benefits of texture analysis of dual energy CT for computer-aided pulmonary embolism detection

  • 1. Benefits of Texture Analysis of Dual Energy CT for Computer–Aided Pulmonary Embolism Detection A. Foncubierta Rodríguez, O. Jiménez del Toro, A. Platon, P.A. Poletti, H. Müller, A. Depeursinge
  • 2. Pulmonary Embolism • Obstruction of arteries in the lungs • Unspecific symptoms • High mortality rates: – 75% (initial hospital admission) – 30% (3 years after discharge) • Delays in diagnosis increase the risk • But easily treated with anticoagulants 2
  • 3. PE Imaging Material Attenuation Coefficient vs keV 0. 1 1 1 0 1 0 0 40 50 60 70 80 90 100 110 120 130 140 Photon Energy (keV) m(E) (cm2/mg) Iodine Water 80 keV 140 keV Conventional CT images • Wedge shaped regions • Heterogeneous attenuation • Correlation with vascularization and ventilation Dual Energy CT images • 4D Data • X,Y,Z • Energy level • Different materials: different attenuations 3
  • 4. Dataset • 25 patients • Image resolution • 0.83mm/voxel (axial plane) • 1mm inter-slice distance • 1.25mm slice thickness • 11 energy levels • Manually segmented lobes • Qanadli index 4
  • 5. Pipeline • Automatic regions of interest • Region-level features: energy of wavelets • Lobe-level descriptors: Bag of visual words • One vocabulary per energy level 3D Analysis • Histogram of visual words for all energy- level vocabularies • Find optimal combination of energy-level vocabularies 4D data integration: 5
  • 6. Automatic ROIs • Saliency-based: – 3D Difference of Gaussians – Multiple scales – Geodesic regional extrema • Data-driven region shape • Local to global analysis of the lobes 6
  • 7. Region-level Features 4 dimensional feature vector per region Energy in Regions 4 scales 3D DoG 7
  • 8. Bag of visual words • BOVW allows data-driven features: – Patterns actually occurring in the data • Vocabularies – K-means clustering – 5 to 25 words – One vocabulary per energy level – Lobe specific: lobes are not directly comparable • Each lobe described by 11 histograms of VW 8
  • 9. Evaluation • Classification based on 1-NN – Q_i > 0 – Q_i < 0 • Leave One Patient Out • Combinations: – From 1 to 11 energy levels – 5 to 50 visual words per energy level • Reference: 70 KeV for conventional CT 9
  • 10. Results Lobe 4D Analysis Accuracy Energy levels Visual words Conventional Accuracy Lower Right 84% 50+130 KeV 5 52% Lower Left 84% 100+140 KeV 5 48% Middle Right 80% 40+50+130+140 Kev 5 52% Upper Left 76% 40+70+80+90 Kev 25 60% Upper Right 80% 90+120 KeV 25 56% 10
  • 11. Conclusions • Using 4D analysis of DECT outperforms conventional CT: 36% accuracy increase • Consistent results among all lobes • Lobe specificities: – No optimal parameters for all lobes – Methods need to be optimized per lobe • Satisfactory results for integration of automatic ROI detection 11
  • 12. Future work Larger database • Ongoing process Similarity- based retrieval • Qanadli index as metric Optimize BOVW • Synonyms 12
  • 13. Thanks for your attention! Questions? A. Foncubierta-Rodríguez, O. Jiménez del Toro, A. Platon, P.A. Poletti, H.Müller and A. Depeursinge, Benefits of texture analysis of dual energy CT for computer- aided pulmonary embolism detection, in: The 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2013), Osaka 2013